What is evaluation in supervised learning?

What is evaluation in supervised learning?

Evaluation in supervised learning refers to the process of assessing the performance of a model using metrics like accuracy, precision, and recall.

Why is it important to evaluate a model?

Evaluating a model is crucial to ensure it generalizes well to unseen data and meets the desired performance criteria.

What are some common evaluation metrics?

Common evaluation metrics include accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC).

How do I choose the right metric for my model?

Choosing the right metric depends on the problem type and the specific goals, such as whether you prioritize false positives or false negatives.

Can you explain cross-validation?

Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent dataset.